Abstract
Pedestrian detection is a hot topic in the field of computer vision in recent year. But the current studies about pedestrian detection mainly focus on feature extraction, training and classifier model and pay little attention to non-maximum suppression (NMS). This thesis uses the information like ratio of detection scores, neighborhood window to improve NMS based on HOG-SVM algorithm, solving the problems that alone windows in detected images arise false detection rate and the suppression windows surrounded by inhibited windows arise false detection rate and missing detection rate. Experiment results on the INRIA pedestrian database show that the improved non-maxima suppression can solve the above problems, reducing the false detection rate and missing detection rate in pedestrian detection.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant: 61376028).
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Wang, Q., Xu, M., Guo, A., Ran, F. (2016). Improvement of Non-maximum Suppression in Pedestrian Detection Based on HOG Features. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 646. Springer, Singapore. https://doi.org/10.1007/978-981-10-2672-0_31
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DOI: https://doi.org/10.1007/978-981-10-2672-0_31
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